Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding
Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2015
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/68458 |
| _version_ | 1855542607239708672 |
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| author | Grenier, Cécile Cao, Tuong-Vi Ospina, Yolima Quintero, Constanza Châtel, Marc H. Tohme, Joseph M. Courtois, Brigitte Ahmadi, Nourollah |
| author_browse | Ahmadi, Nourollah Cao, Tuong-Vi Châtel, Marc H. Courtois, Brigitte Grenier, Cécile Ospina, Yolima Quintero, Constanza Tohme, Joseph M. |
| author_facet | Grenier, Cécile Cao, Tuong-Vi Ospina, Yolima Quintero, Constanza Châtel, Marc H. Tohme, Joseph M. Courtois, Brigitte Ahmadi, Nourollah |
| author_sort | Grenier, Cécile |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed. |
| format | Journal Article |
| id | CGSpace68458 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace684582025-03-13T09:45:54Z Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding Grenier, Cécile Cao, Tuong-Vi Ospina, Yolima Quintero, Constanza Châtel, Marc H. Tohme, Joseph M. Courtois, Brigitte Ahmadi, Nourollah phenotypes rice orysa sativa forecasting genomic plant breeding fenotipos arroz técnicas de predicción genómica fitomejoramiento Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed. 2015-08 2015-10-07T16:00:43Z 2015-10-07T16:00:43Z Journal Article https://hdl.handle.net/10568/68458 en Open Access Public Library of Science Grenier, Cécile; Cao, Tuong-Vi; Ospina, Yolima; Quintero, Constanza; Châtel, Marc Henri; Tohme, Joe; Courtois, Brigitte; Ahmadi, Nourollah. 2015. Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding . PLoS One 10(8): e0136594. |
| spellingShingle | phenotypes rice orysa sativa forecasting genomic plant breeding fenotipos arroz técnicas de predicción genómica fitomejoramiento Grenier, Cécile Cao, Tuong-Vi Ospina, Yolima Quintero, Constanza Châtel, Marc H. Tohme, Joseph M. Courtois, Brigitte Ahmadi, Nourollah Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| title | Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| title_full | Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| title_fullStr | Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| title_full_unstemmed | Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| title_short | Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| title_sort | accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding |
| topic | phenotypes rice orysa sativa forecasting genomic plant breeding fenotipos arroz técnicas de predicción genómica fitomejoramiento |
| url | https://hdl.handle.net/10568/68458 |
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